A deep spectral prediction network to quantitatively determine heavy metal elements in soil by X-ray fluorescence-Reference-Cited by-同舟云学术

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A deep spectral prediction network to quantitatively determine heavy metal elements in soil by X-ray fluorescence

Published:2024 Issue:2 Volume:39 Page:478-490
ISSN:0267-9477
Container-title:Journal of Analytical Atomic Spectrometry
language:en
Short-container-title:J. Anal. At. Spectrom.

Author:

Zhang Qinglun¹²^ORCID,Li Fusheng¹²^ORCID,Yang Wanqi¹²^ORCID

Affiliation:

1. School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China

2. Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, P. R. China

Abstract

A deep spectral prediction network is proposed for the determination of soil heavy metal element concentrations.

Funder

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/JA/D3JA00392B

Reference38 articles.

1. Uncertainty indication in soil function maps – transparent and easy-to-use information to support sustainable use of soil resources

2. A review of soil heavy metal pollution from industrial and agricultural regions in China: Pollution and risk assessment

3. Assessment of Soil-Heavy Metal Pollution and the Health Risks in a Mining Area from Southern Shaanxi Province, China

4. Detection of environmental pollutant cadmium in water using a visual bacterial biosensor

5. Risk of soil contamination by heavy metals through gas-dust emissions

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An integrated CBLA-Net with fractional discrete wavelet transform and frequency-based CARS to predict heavy metal elements by XRF;Analytica Chimica Acta;2024-09

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